AI Security Landscape

AI systems face unique security challenges: data privacy violations, model theft, adversarial attacks, and compliance risks. 43% of organizations experienced AI-related security incidents in 2024, costing an average of ₹3.2Cr per breach.

Key Security Concerns

1. Data Privacy & Protection

• Training Data Security: Encrypt data at rest and in transit
• PII Protection: Anonymize personally identifiable information
• Data Minimization: Collect only necessary data
• Right to Deletion: Enable data removal on request
• Access Controls: Role-based access to sensitive data

2. Model Security

• Model Theft Prevention: Protect model weights and architecture
• Adversarial Defense: Protect against adversarial examples
• Model Inversion Attacks: Prevent extraction of training data
• Poisoning Detection: Identify compromised training data
• API Security: Rate limiting, authentication, input validation

3. Compliance Frameworks

• GDPR (EU): Data protection, right to explanation, consent
• CCPA (California): Consumer data rights
• HIPAA (Healthcare): Protected health information security
• ISO 27001: Information security management
• SOC 2: Service organization controls
• AI Act (EU, upcoming): High-risk AI system regulations

4. Explainability & Transparency

• Model Interpretability: SHAP, LIME for prediction explanations
• Decision Transparency: Document decision-making process
• Bias Detection: Monitor for discriminatory outcomes
• Audit Trails: Log all model predictions and data access

Security Best Practices

Data Protection:

• Implement AES-256 encryption for data at rest
• Use TLS 1.3 for data in transit
• Deploy differential privacy techniques (ε < 1.0)
• Regular security audits and penetration testing

Model Protection:

• Store model weights in secure vaults (HashiCorp Vault, AWS Secrets Manager)
• Implement adversarial training with PGD/FGSM attacks
• Use model watermarking for theft detection
• Deploy input validation and sanitization

Access Controls:

• Implement zero-trust architecture
• Multi-factor authentication (MFA) for all access
• Role-based access control (RBAC)
• Regular access reviews and privilege audits

Compliance Checklist

GDPR Compliance:

• ✓ Data processing agreements with vendors
• ✓ Privacy impact assessments for high-risk AI
• ✓ Right to access, rectification, deletion
• ✓ Data breach notification within 72 hours
• ✓ Consent management system
• ✓ Privacy by design principles

HIPAA Compliance (Healthcare AI):

• ✓ Business Associate Agreements (BAAs)
• ✓ PHI encryption and access logs
• ✓ HITECH Act audit controls
• ✓ Minimum necessary standard

Technology Stack

• Encryption: AWS KMS, Azure Key Vault, HashiCorp Vault
• Privacy: PySyft (differential privacy), TensorFlow Privacy
• Security Scanning: Snyk, Dependabot, OWASP ZAP
• Monitoring: Splunk, Datadog, AWS GuardDuty
• Compliance: OneTrust, TrustArc, Privitar

Case Study: Healthcare AI Platform

• Requirements: HIPAA + GDPR compliance, process 500K patient records
• Solution: End-to-end encryption, differential privacy, federated learning
• Implementation:
  • De-identification pipeline (99.8% PII removal)
  • Federated learning (no raw data leaves hospitals)
  • Differential privacy (ε = 0.8)
  • Automated compliance reporting
• Results:
  • Zero data breaches (2+ years)
  • HIPAA + GDPR certified
  • 98% model accuracy maintained despite privacy constraints
  • Audit time: 8 weeks → 2 weeks (automated reporting)

Common Vulnerabilities

1. Adversarial Examples: Inputs designed to fool models (add adversarial training)
2. Model Inversion: Reconstructing training data (use differential privacy)
3. Data Poisoning: Malicious training data (implement data validation)
4. API Abuse: Scraping models via API (rate limiting, monitoring)
5. Prompt Injection: LLM jailbreaking (input sanitization, system prompts)

Pricing

• Security Assessment: ₹8-15L (one-time)
• Implementation: ₹25-60L (encryption, privacy, compliance)
• Ongoing Monitoring: ₹10-30L/year (tools + personnel)
• Compliance Certification: ₹15-40L (SOC 2, ISO 27001)